scholarly journals Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Nabeel H. Alharthi ◽  
Sedat Bingol ◽  
Adel T. Abbas ◽  
Adham E. Ragab ◽  
Ehab A. El-Danaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Feed Rate ◽  
Experimental Tests ◽  
Surface Topology ◽  
Coefficient Of Determination ◽  
Depth Of Cut ◽  
Artificial Neural

In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.

scholarly journals Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nabeel H. Alharthi ◽  
Sedat Bingol ◽  
Adel T. Abbas ◽  
Adham E. Ragab ◽  
Mohamed F. Aly ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Parameters Optimization ◽  
Cutting Conditions ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Ann Models ◽  
Artificial Neural

All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coefficient of determination.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

Artificial Intelligence Model of Surface Roughness for End Milling Operation of Steel and its Verification by Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 3459-3464
Author(s):  
Mohammed Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
End Milling ◽  
Depth Of Cut ◽  
Efficient Production ◽  
Statistical Experimental Design ◽  
Artificial Neural ◽  
Coated Carbide

Surface roughness is important for evaluating the machined surface quality. In this work, an Artificial Neural Network (ANN) surface roughness prediction model was developed by coupling it with Response Surface Methodology (RSM). For this interpretation, advantages of statistical experimental design techniques, experimental measurements, and artificial neural network were exploited in an integrated manner. Cutting experiments were designed based on small centre composite design technique to develop a RSM model. The input cutting parameters were: cutting speed, feed, and axial depth of cut, and the output parameter was surface roughness. The predictive model was created using a feed-forward back-propagation neural network exploiting the experimental data. The network was trained with pairs of inputs/outputs datasets generated by end milling medium carbon steel with TiN coated carbide inserts. The model can be used for the analysis and prediction of the complex relationships between cutting conditions and surface roughness, in metal-cutting operations, with the ultimate goal of efficient production. The ANN model was verified with the optimized parameters predicted by a coupled genetic algorithm (GA) and RSM technique also developed by the authors.

scholarly journals Prediction of Mechanical Strength of Fiber Admixed Concrete Using Multiple Regression Analysis and Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
S. Karthiyaini ◽  
K. Senthamaraikannan ◽  
J. Priyadarshini ◽  
Kamal Gupta ◽  
M. Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Mechanical Strength ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Mean Squared Error ◽  
Fiber Reinforced Concrete ◽  
Coefficient Of Determination ◽  
Artificial Neural

The present study is to compare the multiple regression analysis (MRA) model and artificial neural network (ANN) model designed to predict the mechanical strength of fiber-reinforced concrete on 28 days. The model uses the data from early literatures; the data consist of tensile strength of fiber, percentage of fiber, water/cement ratio, cross-sectional area of test specimen, Young’s modulus of fiber, and mechanical strength of control specimen, and these were used as the input parameters; the respective strength attained was used as the target parameter. The models are created and are used to predict compressive, split tensile, and flexural strength of fiber admixed concrete. These models are evaluated through the statistical test such as coefficient of determination (R2) and root mean squared error (RMSE). The results show that these parameters produce a valid model through both MRA and ANN, and this model gives more precise prediction for the fiber admixed concrete.

Influence of surface roughness in turning process — an analysis using artificial neural network

2019 ◽  
Vol 43 (4) ◽  
pp. 509-514 ◽  
Author(s):  
B. Radha Krishnan ◽  
V. Vijayan ◽  
T. Parameshwaran Pillai ◽  
T. Sathish
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Cnc Machining ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Vision Measurement ◽  
Artificial Neural

This paper presents methodology to identify the surface roughness value in CNC machining process using a soft computing approach. The aim of this paper is to achieve a roughness accuracy value above 95% and reduce the error rate to below 5% by using an artificial neural network. An artificial neural network method was selected to improve the time of inspection. Fourier transformation method will be used to extract the turning workpiece image, which is the squared value of the major frequency and principal component magnitude. Primary machining parameters such as feed rate, depth of cut, speed, frequency range, gray scale value, and conventional measurement value feed are used as the training input in the artificial neural network. Based on the training sample, the artificial neural network generates the vision measurement value for the testing samples that is compared to the stylus probe measurement value to predict the error rate and accuracy. The novelty of this work is to create an effective methodology using artificial neural network techniques to detect surface roughness errors of materials used in manufacturing industries.

scholarly journals Prediction of Surface Roughness of an Abrasive Water Jet Cut Using an Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3108
Author(s):  
Mirko Ficko ◽  
Derzija Begic-Hajdarevic ◽  
Maida Cohodar Husic ◽  
Lucijano Berus ◽  
Ahmet Cekic ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Mean Squared Error ◽  
Water Jet ◽  
Depth Of Cut ◽  
Abrasive Water Jet ◽  
Ann Model ◽  
Artificial Neural

The study’s primary purpose was to explore the abrasive water jet (AWJ) cut machinability of stainless steel X5CrNi18-10 (1.4301). The study analyzed the effects of such process parameters as the traverse speed (TS), the depth of cut (DC), and the abrasive mass flow rate (AR) on the surface roughness (Ra) concerning the thickness of the workpiece. Three different thicknesses were cut under different conditions; the Ra was measured at the top, in the middle, and the bottom of the cut. Experimental results were used in the developed feed-forward artificial neural network (ANN) to predict the Ra. The ANN’s model was validated using k-fold cross-validation. A lowest test root mean squared error (RMSE) of 0.2084 was achieved. The results of the predicted Ra by the ANN model and the results of the experimental data were compared. Additionally, as TS and DC were recognized, analysis of variance at a 95% confidence level was used to determine the most significant factors. Consequently, the ANN input parameters were modified, resulting in improved prediction; results show that the proposed model could be a useful tool for optimizing AWJ cut process parameters for predicting Ra. Its main advantage is the reduced time needed for experimentation.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

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